gapup-mcp

cve_security_lookup

Read-only

Look up CVE vulnerability data for enterprise security teams, DevSecOps and SOC analysts. Supports two modes: exact CVE ID lookup (e.g. 'CVE-2024-3094') or keyword search by product/vendor (e.g. 'openssl', 'Apache Tomcat'). Cross-references four authoritative keyless sources: NVD NIST (official CVE database, CVSS v3 scores, affected CPEs), CISA KEV (Known Exploited Vulnerabilities catalog — exploit_in_wild flag), EPSS FIRST (exploit probability 0-1), GitHub Security Advisories (ecosystem-specific: npm/pypi/maven). Returns structured vulnerability records with CVSS v3 scores, affected product version ranges, CWE weakness classification, references and exploitation status. Signals engine produces P0/P1/P2 alerts: P0=CVSS>=9 + active exploitation, P1=CVSS>=7 or EPSS>=70%, P2=CWE pattern clusters. Relevant for EU NIS2 and DORA supply chain risk obligations. Optional env: NVD_API_KEY (raises NVD rate-limit 5→50 req/30s), GITHUB_TOKEN (raises GHSA GraphQL rate-limit). Cache TTL 6h. SLA <=25s p95.

Input Schema

TableJSON Schema

Name	Required	Description
`mode`	No	Override auto-detection: "lookup" for exact CVE ID, "search" for product/vendor keyword.
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`query`	Yes	CVE ID (e.g. "CVE-2024-3094") or product/vendor keyword (e.g. "openssl", "Apache Tomcat"). Mode is auto-detected from the CVE-YYYY-XXXXX pattern.
`max_results`	No	Maximum number of vulnerabilities to return (default 20, max 50).
`severity_min`	No	Minimum CVSS v3 severity to include in results (default: no filter).
`published_after`	No	ISO date YYYY-MM-DD — only include CVEs published after this date. Defaults to 365 days ago for search mode.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	Yes
`query`	Yes
`status`	Yes
`signals`	Yes
`sources`	Yes
`quality_score`	Yes
`vulnerabilities`	Yes

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only, non-destructive, open-world behavior. The description adds significant detail beyond annotations: rate-limit behavior with optional API keys, cache TTL (6h), SLA (≤25s p95), and the alerting logic (P0/P1/P2). No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is detailed but well-structured, starting with the main function and progressively adding context. It could be slightly more concise (e.g., mentioning NIS2/DORA obligations might be extraneous), but every sentence contributes to understanding. Front-loaded with key information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 6 parameters, an output schema, and no nested objects, the description covers all essential aspects: modes, data sources, alerting, rate limits, caching, and SLA. It is fully self-contained and leaves no ambiguity about tool behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All 6 parameters have schema descriptions (100% coverage). The description adds value by explaining mode auto-detection, clarifying defaults (e.g., max_results default 20, published_after default 365 days for search), and providing concrete examples for 'query' parameter usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: lookup CVE vulnerability data via exact CVE ID or keyword search. It specifies the target audience (enterprise security teams, DevSecOps, SOC analysts) and contrasts the two modes with examples, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear guidance on when to use the tool (CVE lookup or keyword search) and includes examples for each mode. However, it does not explicitly differentiate from sibling tools like 'vuln_exploitability_forecast' or 'vuln_patch_priority_engine', which could be mentioned as alternatives for related but distinct tasks.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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